2026-01-21 07:52:53
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Netflix shares are trading 37% off their June 2025 peak. That’s the largest downturn for the stock since its 75% collapse in 2022.
But the drivers are very different. In 2022, investors fled due to slower growth post-COVID. Today, the sell-off is a two-part drama:
Profitability scare: Netflix shares stumbled after a Q3 earnings miss, driven by unexpected margin compression and a messy ~$600 million tax dispute in Brazil.
Merger shock: That caution turned into a full-blown exit in December. The pending $83 billion Warner Bros. acquisition introduced complexity, a massive debt load, and integration risks.
All eyes are on the ongoing drama surrounding the merger, with a hostile takeover from Paramount/Skydance and new moving pieces (more on this in a minute).
Today at a glance:
Netflix Q4 FY25.
Warner Drama Update.
Key Earnings Call Quotes.
Ben Affleck vs. The Bitter Lesson.
Revenue +18% Y/Y to $12.1 billion ($80 million beat).
Operating margin 25% (+2pp Y/Y).
EPS $0.56 ($0.01 beat).
Operating cash flow: $10.1 billion (22% margin).
Free cash flow: $9.5 billion (21% margin).
Cash and short-term investments: $9.1 billion.
Debt: $14.5 billion.
Revenue +12%-14% to ~$51.2 billion ($0.2 billion beat).
Operating margin 31.5% (+2pp Y/Y).
📈 Growth accelerates: Revenue growth re-accelerated to +18%, fueled by the ad tier. After stopping regular subscriber updates in 2025, Netflix revealed a new milestone of 325 million paid memberships (representing 8% subscriber growth) and now serves an audience approaching 1 billion people globally.
📢 Ads scale up: The ad business is becoming material, with revenue growing 2.5x Y/Y to $1.5 billion in FY25 (3% of overall revenue). Management noted the ad-supported plan now accounts for over 50% of new sign-ups in available markets, validating the multi-tier strategy. Management expects ad sales to double to ~$3 billion in 2026.
⚠️ Engagement slowing: This is the bearish signal. Despite a massive $18 billion content spend in 2025 (up 11% Y/Y), engagement grew only ~2% in the second half. Management plans to hike spending by another 10% in 2026.
📊 Margins follow seasonality: Operating margin landed at 25%, down sequentially from Q3 (28%) but up +2pp year-over-year. The sequential dip was expected, reflecting the heavy Q4 content slate and marketing push during the holidays. The year-over-year expansion demonstrates continued operating leverage, validating that the margin compression in Q3 was indeed a one-off.
🌍 Sony Pictures deal: A new global exclusive partnership grants Netflix first-window streaming rights for major theatrical releases, including The Legend of Zelda and the Spider-Verse finale, through 2029. Netflix wants to be the inevitable home for Hollywood’s biggest hits immediately after they leave theaters.
🔮 FY26 guidance is noisy: Revenue is expected to rise ~13% Y/Y, with margins expanding, and FCF growing ~16% Y/Y to $11 billion. That said, some costs will be front-loaded, and the Q1 EPS guide fell short of estimates, adding near-term pressure. The focus now shifts to integration, execution, and regulatory approval for the mega-merger.
🏦 Leverage focus: To fund the Warner Bros. deal, Netflix secured $42.2 billion in bridge financing. While the balance sheet currently shows $14.5 billion in debt, this load will increase significantly. Share buybacks are paused indefinitely to hoard cash for the purchase. All eyes will be on the deleveraging path and synergies expected.
In our deep dive in December, we analyzed Netflix’s proposed merger with Warner Bros. We warned then that it was far from a done deal. Six weeks later, the mega M&A move has turned into a trench war on three fronts.
Here’s the state of play as of today and what to make of it.
2026-01-17 23:01:36
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📊 Monthly reports: 200+ companies visualized.
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📩 Saturday PRO reports: Timely insights on the latest earnings.
Today at a glance:
🏛️ Goldman Sachs: Trading Desk Roars
👔 Morgan Stanley: Integrated Machine
📈 Blackrock: Breaking $14 Trillion AUM
🛩️ Delta Airlines: Navigating Headwinds
🍺 Constellation Brands: Relief Rally
🌿 Tilray: International Surge
Goldman Sachs delivered a noisy but ultimately bullish quarter. Revenue fell 3% Y/Y to $13.5 billion ($400 million miss), largely due to a $2.3 billion markdown tied to the Apple Card portfolio exit we discussed here. This caused the revenue from the Platform Solutions segment to turn negative in Q4.
However, the bottom line told a different story. GAAP EPS of $14.01 crushed expectations ($2.25 beat), aided by a massive credit reserve release that more than offset the revenue hit.
Under the hood, the core franchise is firing on all cylinders. Equities trading revenue (included in the Global Banking & Markets segment) rose 25% Y/Y to $4.3 billion, cementing Goldman’s dominance in volatile markets. Investment Banking fees also climbed 25% Y/Y to $2.6 billion, driven by a resurgence in advisory and debt underwriting. The firm is successfully pivoting back to its Wall Street roots, with the Global Banking & Markets division posting record full-year revenues of $41.5 billion.
CEO David Solomon signaled that the strategic “narrowing” is complete, raising the quarterly dividend to $4.50 and unveiling ambitious new targets for the Asset & Wealth Management unit (aiming for a 30% pre-tax margin). With the consumer lending distraction largely resolved and an M&A backlog at a four-year high, Goldman is effectively clearing the decks to ride the wave of a potential 2026 IPO and dealmaking boom.
Morgan Stanley capped off a record year with Q4 revenue rising 10% Y/Y to $17.9 billion ($140 million beat) and GAAP EPS of $2.68 ($0.26 beat).
The results demonstrated significant operating leverage, with the firm delivering a robust Return on Tangible Common Equity (ROTCE) of 21.8% and an improved efficiency ratio of 68%.
2026-01-16 21:02:07
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If the AI era is an arms race, TSMC is the arsenal builder everyone depends on. It has now become the 6th most valuable company in the world, right behind Amazon.
In Q3, the narrative was about conviction. In Q4, that conviction translated into the largest capital expenditure plan in the company's history: $52 billion to $56 billion for 2026 (up 30% year-over-year).
When asked if the demand is real, CEO C.C. Wei highlighted the stakes:
“If we don’t do it carefully, that’d be a big disaster.”
You don’t spend $56 billion on a hunch. A misstep here would result in empty factories and massive losses. By pulling the trigger, management signals they have clear visibility. They are building because the orders are already there.
With 2nm production online and gross margins hitting new highs, the foundry is cementing its lead with hard assets.
Disclosure: I own TSM in App Economy Portfolio. It was the January 2023 Stock Idea, and the stock has more than quadrupled since then.
Today at a glance:
⚙️ TSMC’s $56 billion bet
📱 Apple picks Gemini for Siri
🤖 Zuck launches Meta Compute
Income statement:
Revenue rose +25% Y/Y to $33.7 billion ($1.0 billion beat).
Gross margin was 62% (+3pp Y/Y).
Operating margin was 54% (+5pp Y/Y).
EPADR (American Depositary Receipt) was $3.14 ($0.16 beat).
Revenue by platform:
💻 High-Performance Computing (55% of revenue, +2pp Y/Y).
📱 Smartphone (32% of revenue, -3pp Y/Y).
💡 IoT (5% of revenue, flat Y/Y).
🚘 Automotive (5% of revenue, +1pp Y/Y).
🎮 Digital Consumer Electronics (1% of revenue, flat Y/Y).
Others (2% of revenue, flat Y/Y).
Revenue by technology:
3nm (28% of revenue, +2pp Y/Y).
5nm (35% of revenue, +1pp Y/Y).
7nm (14% of revenue, flat Y/Y).
16nm and above (23% of revenue, -3pp Y/Y).
Cash flow:
Operating cash flow margin was 69% (-2pp Y/Y).
Free cash flow margin was 35% (+5pp Y/Y).
Balance sheet:
Cash, cash equivalents, and short-term investments: $97.6 billion.
Long-term debt: $27.2 billion.
Q1 FY26 Guidance:
Revenue ~$35.2 billion ($2.7 billion beat).
Gross margin ~64% (~60% expected).
Operating margin ~55% (~51% expected).
💥 Another double beat: The top-line beat was driven by “insane” AI demand. But the real story is profitability. Gross margin expanded to 62% (up from 60% last quarter), underscoring TSMC’s significant pricing power as customers compete for limited capacity.
🔮 Guidance implies acceleration: Management isn’t seeing a slowdown. For Q1 2026, they expect revenue between $34.6 and $35.8 billion. That’s a massive 38% year-over-year increase at the midpoint. For full-year 2026, they expect revenue to grow by nearly 30% (in USD), significantly outpacing the broader industry forecast of 14%.
🏗️ The $56 billion bet: TSMC raised its 2026 capex budget to $52–$56 billion (up from $41 billion in 2025). About 80% of this is allocated to advanced process technologies. This aggressive spending signals that their customer checks for AI demand over the next 2-3 years are rock solid.
⚙️ The 2nm era begins: While 3nm and 5nm are the current cash cows (combined 63% of revenue), TSMC confirmed that N2 (2nm) entered high-volume manufacturing in Q4 2025 with good yields. They expect a fast ramp in 2026, maintaining their lock on technology leadership against competitors like Intel and Samsung.
🤖 HPC is now the dominant force: High-Performance Computing (AI + 5G) now represents 55% of total revenue, widening the gap with Smartphones (32%). While consumer electronics face headwinds from a memory chip supply crunch, TSMC notes that high-end AI smartphones remain resilient.
🇺🇸 Doubling down on Arizona: The Gigafab plan is expanding. TSMC confirmed the purchase of a second large parcel of land in Arizona to support an independent gigafab cluster. Fab 1 is in high-volume production, and Fab 2 is pulled forward to 2027.
🗣️ The bubble verdict: Addressing fears of overspending, CEO C.C. Wei noted that the capex hike comes after rigorous verification with customers. He stated the silicon supply remains the bottleneck for AI infrastructure, not power, and that the company is working to “close the gap” between supply and demand.
Check out the earnings call transcript on Fiscal.ai here.
Apple has officially selected Google’s Gemini to power the next generation of Siri.
This is a multi-year partnership in which Google’s models and cloud infrastructure will serve as the foundation for Apple’s AI features. Unlike the existing integration with OpenAI (which acts like a “phone a friend” chatbot when Siri is stumped), this deal puts Gemini deep inside Siri’s operating logic.
For over a decade, the money has flowed one way: Google pays Apple (an estimated $20+ billion annually) to be the default search engine on the iPhone.
This deal flips the script, albeit on a smaller scale. Reports suggest Apple will pay Google ~$1 billion per year. For a company like Apple, $1 billion is a rounding error. But it signals a massive strategic pivot. Apple has effectively decided that spending tens of billions on CapEx to train a frontier model from scratch isn’t the best use of its cash.
This is a classic “buy vs. build” decision. By white-labeling Gemini:
Apple (the aggregator): Keeps the direct relationship with the user and the privacy layer (Private Cloud Compute). They capture the value of the interface.
Google (the supplier): Gets massive validation and scale for its models but operates in the background.
You likely won’t see the Gemini logo when you use Siri. Apple is treating Google’s model like a component supplier, similar to how it buys screens from Samsung or camera sensors from Sony. It’s Apple Intelligence on the outside, powered by Google on the inside.
Apple is betting that it doesn’t need to have the smartest model in the world at any given time. It just needs one that is reliable.
Risk: Apple is now dependent on a rival for a core AI competency.
Reward: Siri will finally become usable for complex tasks without Apple torching its margins on training frontier models.
Reports from the Financial Times suggest OpenAI declined the deal. Sam Altman reportedly refused to become a white-label utility, prioritizing compute for OpenAI’s own future hardware with Jony Ive. This leaves Google to serve as Apple’s invisible backend.
🔮 What to watch Google recently surpassed Apple in market cap for the first time since 2019. With this deal, Google secures its place as the AI utility layer for the world’s most premium hardware. The frenemies remain closer than ever.
While Apple is outsourcing its AI brain to Google to stay asset-light, Meta is going asset-heavy on a scale that is hard to comprehend.
Meta has established Meta Compute, a new top-level division dedicated to building the physical backbone of the AI era.
Zuckerberg is splitting his infrastructure strategy into two clear lanes:
Technical (now): Led by Santosh Janardhan, focusing on the actual data center architecture, silicon, and day-to-day operations of the fleet.
Supply Chain (future): Led by Daniel Gross (who co-founded Safe Superintelligence with Ilya Sutskever), focusing on securing the supply chain and business models needed to build at this scale.
Crucially, Meta is not trying to become AWS. Zuck isn’t building these data centers to rent servers to startups (a low-margin game where AWS, Azure, and GCP already won).
Zuck might have PTSD from Apple's App Tracking Transparency, which nearly derailed his business. He is looking far ahead to ensure history doesn’t repeat itself.
Meta is betting that compute (not models) will be the scarce resource of the next decade. By owning the power plants and the silicon, Meta ensures it never has to beg Google or Microsoft for capacity to run its Personal Superintelligence features.
Wall Street generally hates it when Zuckerberg spends billions on sci-fi projects. To buy their patience for this new AI splurge, he had to offer a sacrifice.
Meta is cutting ~10% of Reality Labs (the Metaverse division) and closing studios like Twisted Pixel and Sanzaru.
This is the official pivot from Metaverse to AI. The dream of living in VR isn’t dead, but it is being deprioritized to fund the Gigawatt buildout.
Meta has a critical advantage over OpenAI because the ad business already generates massive free cash flow. By refocusing his effort on AI, Zuck can afford to burn billions on GPU clusters while his core business pays the bills.
You will hear this term a lot. Zuckerberg stated Meta plans to build “tens of gigawatts” of capacity this decade.
What is a Gigawatt (GW), you ask? It’s roughly the output of a standard nuclear power plant. That’s enough energy to power ~750,000 homes.
Meta is effectively trying to build the equivalent of 10 to 50 nuclear power plants’ worth of compute capacity.
To execute this, Zuckerberg just hired Dina Powell McCormick as President and Vice Chair. She was previously a partner at Goldman Sachs and a Trump Deputy National Security Advisor.
Why? Because you don’t build all this nuclear power by writing code. You do it by navigating complex government regulations and sovereign wealth deals. She is the political bridge to the physical power Meta needs.
Meta also announced huge deals with three nuclear energy providers this week: Vistra, TerraPower (Bill Gates-backed), and Oklo (Sam Altman-backed). The goal is to add 6.6 GW of nuclear capacity by 2035.
This is a race against physics and regulation. By hiring a Trump-era diplomat, Zuck wants to ensure the regulatory environment is favorable to this massive buildout.
That's it for today.
Happy investing!
Thanks to Fiscal.ai for being our official data partner. Create your own charts and pull key metrics from 50,000+ companies directly on Fiscal.ai. Start an account for free and save 15% on paid plans with this link.
Disclosure: I own AAPL, AMZN, GOOG, META, NVDA, and TSM in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-01-14 23:38:07
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A new earnings season is here!
As always, it starts with the big banks. 🏦
The banking sector’s final report card of 2025 has been largely overshadowed by the unprecedented clash in Washington. The DOJ launched a criminal investigation into Fed Chair Jerome Powell over headquarters renovation costs. This move is widely seen as a pretext to force aggressive rate cuts. Powell didn’t blink, issuing a defiant defense of central bank independence just months before his term expires.
But wait, there’s more! President Trump doubled down on his demand for a 10% cap on credit card rates by January 20. The proposal triggered an immediate sell-off in bank stocks, with lenders warning the move would force them to slash credit lines and eliminate rewards programs to stay profitable.
Against this chaotic backdrop, the latest earnings signaled that while the economic engine is humming, the gears are grinding. The much-anticipated explosion in dealmaking hit an air pocket as tariff anxieties pushed mergers into 2026, while rising costs and the sudden regulatory fears are weighing on valuations even as profits remain robust.
Let’s break down the results.
Today at a glance:
JPMorganChase: Apple Card Takeover
Bank of America: Equities Surge
Wells Fargo: Growing Pains
Citigroup: Transformation Takes Shape
As a reminder, banks make money through two main revenue streams:
💵 Net Interest Income (NII): The difference between interest earned on loans (like mortgages) and interest paid to depositors (like savings accounts). It’s the primary source of income for many banks and depends on interest rates.
👔 Noninterest Income: The revenue from services unrelated to interest. It includes fees (like ATM charges), advisory services, and trading revenue. Banks relying more on noninterest income are less affected by interest rate changes.
Here are the significant developments shaping Q4 FY25:
📉 Dealmaking is lumpy: The animal spirits recovery is proving uneven. While JPMorgan and Bank of America saw advisory fee activity stall amid market volatility, Citigroup defied the trend, posting an 84% surge in advisory fees. The M&A rebound is real, but it’s going to be lumpy.
🎢 Equity traders save the day: Volatility was a headache for dealmakers but a goldmine for traders. JPMorgan (+40%) and Bank of America (+23%) posted massive jumps in equity trading revenue, helping offset weakness elsewhere. However, those reliant on Fixed Income (like Citi) saw less upside.
🏛️ The “Trump Trade” cuts both ways: The post-election optimism has curdled into regulatory anxiety. While banks expected a deregulatory bonanza, the proposed 10% credit card interest cap triggered a unified warning. From JPMorgan’s “everything is on the table” defense to Citi’s blunt “we could not support it,” the industry is bracing for a fight.
💸 Expense strategies diverge: The sector is splitting on costs. JPMorgan stunned Wall Street with a massive $105 billion spending plan to widen its moat. In contrast, Citi and Wells Fargo are still in “cutting mode,” shedding thousands of jobs and taking severance charges to protect margins.
💳 Soft landing still in play: Despite fears of a softening labor market, the data remains solid. Card spending is up across the board, and delinquencies are stable or even improving. The recession ghost story continues to be pushed out, providing a sturdy floor for the sector.
🔑 Takeaway: The straight line up narrative is over. Banks are navigating a complex mix of booming trading floors, uneven deal flow, and divergent cost strategies, all while keeping one eye on a hostile transition of power at the Federal Reserve.
Let’s visualize them one by one and highlight the key points.
Net revenue grew 7% Y/Y to $45.8 billion ($0.5 billion beat):
Net interest income (NII): $25.0 billion (+7% Y/Y).
Noninterest income: $20.8 billion (+7% Y/Y).
Net income: $13.0 billion (-7% Y/Y).
Adjusted EPS: $5.23 ($0.37 beat).
Key developments:
📱 Apple Card impact: The bottom line took a heavy hit from a $2.2 billion credit reserve build tied to taking over the Apple Card program from Goldman Sachs. This strategic move dragged EPS down by $0.60, though management views the upfront pain as necessary for the 2-year integration process.
📊 Trading saves the quarter: While many peers struggled, JPMorgan's Markets revenue surged 17% to $9.7 billion, beating the highest analyst estimates. The beat was driven by a 40% jump in equities trading and strong fixed-income results, capitalizing on market volatility.
📉 Investment Banking miss: Contrary to guidance given just last month, Investment Banking fees fell 4% to $2.3 billion. The bank cited delayed deals and a surprise 2% decline in debt underwriting (analysts expected a 19% gain). CFO Jeremy Barnum noted, "Our performance was not what we would have liked."
💳 Consumers stay spending: Despite macro headlines, debit and credit card spending rose 7% Y/Y. Credit quality remains stable with delinquencies actually dropping slightly to 1.10% (vs 1.14% last year).
🌐 Economy resilient: The bank is positioning for a soft landing in which labor softens while spending continues. Jamie Dimon highlighted “huge” geopolitical risks.
🔑 Takeaways: A messy quarter. The core business is split: Trading is firing on all cylinders, but Investment Banking stumbled unexpectedly. The headline earnings miss was largely manufactured by the massive Apple Card.
Key quote:
CEO Jamie Dimon: “The US economy has remained resilient. While labor markets have softened, conditions do not appear to be worsening. Meanwhile, consumers continue to spend, and businesses generally remain healthy.”
2026-01-09 21:01:05
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It’s the single most persistent question in the market today. CapEx is ramping, valuations are soaring, and the hype is deafening. Yet the earnings are real, margins are expanding, and demand is unprecedented.
That’s the tension Howard Marks addresses in his latest memo.
Marks is the co-founder of Oaktree Capital and one of the most respected investors of our time. His memos are thoughtful, long-cycle reflections on risk, psychology, and capital allocation.
Warren Buffett once put it plainly:
“When I see memos from Howard Marks in my mail, they’re the first thing I open and read. I always learn something.”
In his new memo, Marks cuts through the noise surrounding the AI boom. Rather than trying to forecast prices or call a market top, he examines behavior.
There are really two conversations happening at once:
• The scale and pace of the AI infrastructure buildout.
• How that buildout is being priced in public and private markets.
Marks’ conclusion is deliberately measured, but unmistakably cautious. If AI enthusiasm doesn’t produce a bubble, he writes, it will be a first.
In this article, we’ll pull out the memo’s most important insights and pair them with visuals to help frame where we are in the cycle.
Not to call a top. Not to predict what happens next. But to understand how bubbles work and what to do about them.
Today at a glance:
How Bubbles Form
Two Types of Bubbles
The Uncertainty Stack
The Debt Problem
How Investors Should Think
Bubbles don’t begin with bad ideas.
They begin with good ideas taken too far.
A new technology appears and feels genuinely different. It promises to change how the world works. Early adopters are rewarded, often dramatically. Those early successes validate the story, and that’s when excitement turns into FOMO (fear of missing out). This is true for both the industry (AI chips) and the market (AI stocks).
People stop asking “Is this real?” and start asking “How do I get exposure?”
Skepticism feels costly. Caution feels like falling behind.
CEOs, from Mark Zuckerberg and Sundar Pichai, have made clear that underinvesting in AI is a far greater risk than overinvesting. The danger arises when individual investors adopt a similar mindset, even when they face no existential threat.
I recreated this classic market psychology chart (the original from Wall Street Cheat Sheet is hard to read, and the website is no longer maintained). It captures the raw emotions that repeat across every market cycle. I’ll leave it to you to decide where we are today.
At the core, bubbles aren’t caused by technology itself.
They’re caused by excessive optimism applied to something new.
Newness matters because it removes constraints. With no history to anchor expectations, the future feels limitless. In turn, valuations stretch beyond what can be justified by predictable earnings power, not because investors are foolish, but because imagination fills the gaps where data doesn’t yet exist.
That’s why bubbles follow such a consistent pattern.
Human psychology doesn’t change.
An early success breeds confidence.
Confidence turns into extrapolation.
Extrapolation invites speculation.
Speculation lowers standards.
Eventually, prices stop reflecting likely outcomes and start reflecting potential.
One of Howard Marks’ most important observations is that debating whether something is a bubble can distract from better judgment. You don’t need a label to behave intelligently. You just need to recognize when the pendulum has swung too far.
That’s usually when we see:
IPOs and private funding rounds surge, often at extreme valuations.
Narratives overpower fundamentals, with stories running ahead of cash flows.
Returns concentrate in a few stocks, pulling in passive and momentum capital.
FOMO-driven speculation replaces risk assessment.
Financial engineering fills the gaps through leverage or circular deals.
All of those ingredients are undeniably present in today’s market.
As Sir John Templeton famously warned, the four most dangerous words in investing are: “This time it’s different.”
AI may indeed be transformative, and the massive CapEx ramp justified.
But when success is priced as inevitable, future returns tend to disappoint.
Not all bubbles are the same.
One of the most useful distinctions in Marks’ memo comes from a simple idea: some bubbles form around real technological inflections, while others are built almost entirely on financial excess.
Understanding which one you’re dealing with matters far more than debating whether something is “a bubble” at all.
These are the destructive kind.
They’re fueled by financial engineering, leverage, or the promise of returns without risk. Nothing fundamental has changed in the real economy. When enthusiasm fades, prices revert, and little of lasting value remains.
Think subprime mortgages, portfolio insurance, or other fads that rise and fall without moving the world forward. These bubbles destroy wealth, full stop.
They form around technologies that genuinely change society: railroads, electricity, aviation, the internet, and now AI.
In these cases, the direction is right. The timing and pricing are often wrong.
In inflection bubbles, capital arrives faster than the technology can mature. Infrastructure is overbuilt, competition intensifies, and returns collapse, even as real-world adoption accelerates.
The world moves forward. But not all investors remain unscathed.
Marks makes a crucial point here: inflection bubbles can accelerate progress precisely because they waste capital. The speculative mania compresses decades of experimentation, trial-and-error, and infrastructure buildout into a short period. Much of the money is lost, but the foundation for future productivity is laid.
That creates a paradox investors often miss.
A technology can be world-changing and a terrible investment at certain prices.
Progress for society does not guarantee profits for shareholders.
AI clearly fits the inflection-bubble category. Its potential is real, and its impact is hard to dispute. But that doesn’t make every investment tied to AI sensible, or every valuation defensible.
Which leads to the most uncomfortable part of inflection bubbles: the end state is unknowable.
The defining feature of inflection bubbles is their unknowability.
Analysts already struggle with forecasting growth rates or margins for the next quarter. They’re being asked to price an end state that doesn’t yet exist, and may look nothing like today’s assumptions.
Google’s stock swung from uninvestable to inevitable in just a few years. Not because the future became predictable, but because new facts kept changing the narrative.
Howard Marks returns to this point repeatedly in his memo: the biggest risk isn’t being wrong about AI’s importance, but overestimating how much can be known in advance.
Technologies don’t move through the cycle uniformly. The Gartner Hype Cycle reminds us that expectations, adoption, and economic maturity rarely advance in lockstep. In AI, different layers are likely sitting in very different places, which makes pricing the “end state” especially fragile.
Start with the most basic question.
Who actually wins? History is brutal here. Revolutionary technologies don’t reward early leaders by default. Railroads, autos, search, and social media all reshuffled incumbents. Some of today’s frontrunners may dominate. Others may be displaced by companies that do not yet exist.
Who captures the profits? Even if AI adoption explodes, that doesn’t mean vendors earn excess returns. Productivity gains can accrue to customers instead. Cost savings may be competed away through lower prices. A technology can transform industries without enriching the companies that provide it.
What does the market structure look like? Monopoly, duopoly, competitive free-for-all, or a layered ecosystem with a few winners and many marginal players? Each outcome supports radically different valuations, yet markets often price one as if it’s inevitable.
How durable are today’s assets? Chips, data centers, and models are expensive, with a useful life still TBD. Rapid innovation increases the risk that today’s infrastructure becomes obsolete before it recoups its cost. That matters enormously when assigning multiples or underwriting long-term cash flows.
How much of the growth is real demand? Marks highlights the risk of circular behavior: vendors selling to customers who are simultaneously funding them, or partners transacting to show progress. Activity can look explosive without being durable. To be sure, Goldman Sachs estimates that less than 15% of NVIDIA’s revenue will come from circular deals in 2027, so it’s not the main story as some analysts make it out to be.
Layer these uncertainties together, and a pattern emerges.
The future may be enormous, but its shape, timing, and economics are deeply unclear.
Most bubbles deflate. Debt is what makes them dangerous.
When outcomes are uncertain, equity absorbs mistakes, delays, and pivots. Debt does not. It assumes cash flows will arrive on time and at scale to service fixed obligations. That’s a reasonable assumption in stable industries. It’s a fragile one in fast-moving technological revolutions.
This is where Howard Marks draws his sharpest line. Financing uncertainty with equity is normal. Financing conjecture with debt is not.
AI infrastructure sits uncomfortably close to that boundary. Chips, data centers, and models are expensive, capital-intensive assets with useful lives that are hard to forecast. Their economics depend on demand that may accelerate, stall, or shift as the technology evolves.
The dot-com era left behind dark fiber, massive infrastructure built for internet traffic that didn’t yet exist. It was a gamble on future demand that arrived too late.
In a recent a16z interview, Gavin Baker put it simply:
“Contrast that with today, there are no dark GPUs.”
AI looks different. We aren’t building ahead of demand. We are chasing it. GPU capacity is heavily utilized, and supply is constrained. Unlike fiber in 2000, today’s compute isn’t sitting idle.
High utilization acts as a floor. It reduces the risk of near-term write-downs and confirms that today’s CapEx is responding to genuine, cash-paying demand. But high utilization only proves the utility is real. It doesn’t prove the profitability is permanent.
And that doesn’t eliminate the role debt can play in turning uncertainty into fragility.
So far, much of the AI capex ramp has been funded internally. The largest platforms generate tens of billions of dollars in annual free cash flow from their existing businesses, allowing them to scale aggressively without immediately stressing their balance sheets.
Even OpenAI has largely relied on equity-based partnerships and long-term commercial agreements to finance its cash burn, avoiding near-term balance-sheet pressure.
At the same time, the bond market has quietly become part of the financing stack.
Meta issued one of the largest corporate bond deals in history, raising roughly $30 billion across long-dated maturities as AI capex accelerated.
Alphabet and Oracle have both issued 30-year bonds in recent years, explicitly extending financing horizons to match long-lived AI and cloud infrastructure.
Amazon, already one of the largest capital spenders in the world, continues to pair massive AWS CapEx with regular access to debt markets to preserve flexibility.
None of this is reckless in isolation. These are high-quality issuers with strong cash flows. Credit markets are open because default risk looks remote.
Compared to past bubbles, the system is less immediately fragile.
But it doesn’t make the capital immune to misallocation.
When CapEx is funded by free cash flow, the risk shifts from insolvency to dilution. Shareholders may avoid catastrophic outcomes while still paying through lower free cash flow, reduced buybacks, or years of subpar returns if investments fail to earn their cost of capital.
This is not 2000, but the buildout still comes at a cost.
We are facing a self-aware bubble today.
Investors openly debate whether the AI boom is a bubble. Valuation multiples are scrutinized. Comparisons to 1999 are everywhere. That awareness doesn’t eliminate excess.
In May 1999, Barron’s ran its now-famous “Amazon.bomb” cover, warning that Amazon’s business model was unproven.
Amazon’s stock fell more than 90% over the next three years.
Twenty-five years later, the stock is up more than 50×.
The lesson: Being right about the destination (AI will change the world) doesn’t protect you from the journey (a potential massive drawdown).
The job isn’t to predict the outcome, but to survive the volatility along the way. That requires conviction without rigidity. Strong views, loosely held. And a willingness to change your mind as facts change.
The hardest part of investing through a potential bubble isn’t data or analysis. It’s behavior. In legendary investor Peter Lynch's terms, the most crucial organ in investing is not the brain. It’s the stomach.
No one knows whether today’s enthusiasm fades quietly or ends painfully. Howard Marks borrows from Mark Twain and argues that history doesn’t repeat, but it often rhymes. Painful endings are more common than gentle ones. The trillion-dollar question is when.
Fed Chair Alan Greenspan famously coined the term “irrational exuberance” in 1996. The SP& 500 more than doubled from here before peaking in March 2000. The lesson is uncomfortable but clear: trying to call market tops is a hazardous hobby. Peter Lynch noted that more money is lost attempting to time corrections than in the corrections themselves.
Staying invested through uncertainty is a prerequisite for compounding returns. Yet it’s the rule most often abandoned.
One practical way to apply a long-term mindset is to assess how much optimism is already embedded in prices, particularly among market leaders. As Marks puts it, valuations have been “high, but not crazy,” particularly relative to the underlying business momentum.
Marks’ advice is deliberately unspectacular: not all-in, not all-out. A moderate position, applied with selectivity and prudence.
AI may prove to be the most important technology of our lifetime. It is also likely to produce excess, overbuilding, and painful corrections.
CEOs have no choice. They must over-invest to avoid extinction. Underinvesting is an existential risk they cannot take.
You do not face that constraint. You can sit on your hands. You can ignore the overpriced IPOs. You can diversify. That flexibility is not a weakness. It is your distinct advantage.
How to use it:
Separate belief from pricing: A company can change the world and still be a terrible investment at the wrong price.
Audit the balance sheet: In a storm, cash is oxygen. When capital becomes expensive, companies that need to borrow to survive often don’t.
Don’t mistake volatility for risk: Big drawdowns are the cost of admission in public markets. What Morgan Housel calls “a feature, not a bug.” You must have the stomach for 30%-50% drops to capture the long-term upside.
Avoid ruin: Stay away from leverage. Missing upside is uncomfortable, but being forced out is fatal. The goal is to stay invested long enough to benefit from what endures.
The investors who endure won’t be the loudest or the boldest. They will be the ones who stayed invested, selective, and humble. Long after the cycle has turned.
That's it for today.
Happy investing!
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Disclosure: I own NVDA, AAPL, GOOG, AMZN, AVGO, and META in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-01-06 21:02:19
Welcome to the Premium edition of How They Make Money.
Over 270,000 subscribers turn to us for business and investment insights.
In case you missed it:
The physical economy includes trucking, construction, and oil and gas. For decades, it has run on paper logs, phone calls, and manual processes.
Motive wants to be the operating system for this physical world.
Fresh off its S-1 filing on December 23, 2025, Motive is set to be one of the first major IPOs of 2026. It’s backed by heavyweight investors, including Google Ventures.
If you follow this space, you’re likely familiar with Samsara (IOT), a close peer that went public in 2021 and a holding in App Economy Portfolio. The two companies sell similar products and target the same fleet-heavy customers. But Motive enters the public markets with a very different financial profile.
Motive is smaller, growing more slowly, burning cash, and carrying meaningful debt.
Is Motive a way to play the digitization of the physical economy, or a runner-up trap in a winner-takes-most market?
I went through the nearly 300 pages of Motive’s S-1 so you don’t have to.
Today at a glance:
Overview
Business Model
Financial highlights
Risks & Challenges
Management
Use of Proceeds
Future Outlook
Personal Take
Motive is an Integrated Operations Platform for the physical economy.
Founded in 2013 by CEO Shoaib Makani as KeepTruckin, the company started with a simple wedge: a free app to help truck drivers log their hours on a smartphone.
The timing mattered. The ELD (Electronic Logging Device) mandate created a rare moment when software adoption became mandatory. When the rule became federal law in 2017, most commercial trucking fleets were pushed to digitize compliance. Motive positioned itself ahead of that shift, landing customers before compliance became mandatory.
Today, they have expanded far beyond compliance logs into a full ecosystem:
Driver Safety: AI dashcams that detect distracted driving, including phone usage and smoking.
Fleet Tracking: GPS tracking for vehicles and heavy equipment.
Spend Management: The Motive Card, a corporate card for fuel and fleet-related expenses that integrates directly with the platform.
Motive’s AI pitch is all about measurable outcomes. The company estimates that its platform has helped prevent more than 170,000 collisions since 2023, underscoring why safety and compliance budgets are often non-discretionary for fleet operators.
$501 million ARR (+27% Y/Y, accelerating).
100,000+ customers (mostly small trucking fleets).
4,500+ employees (heavy R&D presence in Pakistan).
For context, when Samsara went public in 2021, it did so with an ARR of $493 million (comparable to Motive) but was growing at nearly 70% Y/Y.
Motive operates a classic Hardware-Enabled SaaS model.
In short: Hardware is not the margin driver. It’s the lock-in.
Motive’s cameras, sensors, and in-vehicle devices aren’t optimized for margin. They exist to create continuous first-party operational data tied directly to physical activity. Once installed, that hardware makes Motive the system of record for safety and compliance, raises switching costs, and enables multi-year software expansion on top of the same data stream.
The wedge (hardware): Motive sells physical devices, including Vehicle Gateways (ELDs), AI dashcams, and Asset Gateways. These act as the “eyes and ears” of the fleet, continuously capturing data from drivers, vehicles, and equipment. Like Samsara, the hardware is often sold at or near cost to secure the long-term software relationship.
The subscription model (SaaS): Customers pay a per-vehicle, per-month subscription to access Motive’s cloud dashboard. Once installed, removing Motive hardware requires physical replacement across the fleet, making churn operationally painful. This creates high switching costs and predictable recurring revenue.
The fintech layer (transaction revenue): Motive’s newer growth lever is the Motive Card, which earns interchange revenue when drivers pay for fuel or repairs. By tying spend directly to fleet activity, Motive gains visibility into where money is spent, reduces fraud, and adds a transactional revenue stream on top of subscriptions.
The flywheel looks like this: Capture Data (IoT devices) > AI Insights (safety, efficiency) > Financial Services (Motive Card) > More Data.
Core Customers (ARR > $7.5K): 9,201 (+17% Y/Y)
Large Customers (ARR > $100K): 494 (+58% Y/Y).
Net Dollar Retention (NDR): 110% for Core Customers (+1pp Y/Y), and 126% for Large Customers. This shows they are successfully upselling larger fleets (adding dashcams, cards, etc.).
Expansion at Motive is usage-driven, not contract-driven. Revenue scales automatically as fleets grow in vehicles, drivers, assets, and locations. Larger fleets generate more data, adopt more modules, and standardize on fewer systems as operational complexity rises. That dynamic shows up clearly in the numbers, with large customers expanding far faster than smaller ones. Multi-product adoption is steadily improving among Core Customers.
So why is Motive growing at a much slower pace than Samsara at the same ARR milestone? The growth gap stems from the go-to-market strategy. Motive built its business in the SMB segment, initially selling low-ACV (Annual Contract Value) compliance solutions to small fleets. That left it with a large customer base, but lower revenue per account.
Samsara targeted mid-market and enterprise customers earlier, driving larger deal sizes and faster ARR expansion at a similar scale.
Let’s turn to the financials and where the money flows. Motive is growing, but it is burning cash to do it. And that burn is not improving with scale.